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Research papers on AI in medical diagnosis

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  1. Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

    Rawan Ghnemat, Sawsan Alodibat, Qasem Abu Al-Haija · 2023 · Journal of Imaging · 70 citations

    Recently, deep learning has gained significant attention as a noteworthy division of artificial intelligence (AI) due to its high accuracy and versatile applications. However, one of the major challenges of AI is the need for more interpretability, commonly referred to as the black-box problem. In this study, we introduce an explainable AI model for medical image classification to enhance the interpretability of the decision-making process. Our approach is based on segmenting the images to provide a better understanding of how the AI model arrives at its results. We evaluated our model on five datasets, including the COVID-19 and Pneumonia Chest X-ray dataset, Chest X-ray (COVID-19 and Pneum

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  3. Artificial Intelligence in Medical Imaging: Applications of Deep Learning for Disease Detection and Diagnosis

    Abhinav Deshmukh · 2024 · Universal Research Reports · 7 citations

    The integration of artificial intelligence (AI) and deep learning techniques into medical imaging has revolutionized disease detection and diagnosis. This paper provides a comprehensive overview of the applications of deep learning in medical imaging and its impact on healthcare. The paper begins with an introduction to the fundamentals of deep learning, emphasizing convolutional neural networks (CNNs) and their relevance in analyzing medical images. It then explores various applications of deep learning in medical imaging, including automated disease detection and classification, image segmentation for precise anatomical localization, quantitative analysis for predictive modeling, personali

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  8. Deep Learning-Based AI Framework for Automated Medical Diagnosis

    Vishal Khanna · 2026 · i-manager's Journal on Artificial Intelligence & Machine Learning

    The rapid adoption of artificial intelligence (AI) in healthcare has opened new avenues for automated medical diagnosis, offering the potential to significantly improve clinical decision-making, diagnostic accuracy, and early disease detection. With the increasing availability of digital health records, medical imaging, and laboratory data, AI-driven systems are being explored as effective tools to support clinicians in managing complex and large-scale medical information. Traditional diagnostic processes often rely on manual interpretation and expert judgment, which can be time-consuming, subject to human error, and limited by inter-observer variability. In this context, deep learning techn

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  9. Deep Learning for Medical Imaging Analysis

    Gayatri Gupta, Aafila Shrivastava · 2026 · Journal of Artificial Intelligence and Information

    This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis. We examine the evolution of medical image processing from traditional computer vision approaches to sophisticated deep learning solutions, highlighting the critical role of artificial intelligence in modern healthcare diagnostics. Our research encompasses multiple dimensions of medical imaging analysis, including disease detection, segmentation, classification, and automated diagnosis across various imaging modalities. Through extensive evaluation across diverse medical imaging datasets, we demonstrate significant improvements in diagnostic accuracy and efficiency. Our findings show that d

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  11. Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis

    Ohmini Krishnamurthy Rajendran · 2026 · International Journal of Drug Delivery Technology

    Radiological oncology is using AI to analyse complicated images, analyse tumours, and make treatment decisions. This study focuses on deep learning, radiomics, and radiogenomics frameworks for AI applications in breast, lung, prostate, brain, gastrointestinal, and metastatic cancers. Outperforming expert radiologists, these tools have improved lesion identification, segmentation, risk prediction, and molecular phenotype inference. In addition, the review discusses how AI influences workflow triage, report standardization, and radiologist efficiency in digital mammography, MRI, PET/CT, and CT. Despite recent advancements, the need for AI outputs that can be explained in therapeutic contexts,

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